Elaborate computing systems are used to coordinate the display of online ads to visitors of web pages, search engine users, social media users, e-mail recipients, and other electronic device users. In a common example, a merchant (marketer) wanting to reach its customers (visitors) on some other company's website (publisher), like a sport television network's website, does so by bidding on one or more online ad-slots on the publisher's web pages. The publisher web pages have online ad-slots that are commonly auctioned via an online ad-exchange such as the DoubleClick Ad Exchange™ program by Google, Inc. of Mountain View, Calif. Demand Side Platforms (DSPs) such as Adobe® Media Optimizer, by Adobe Systems, Inc. of San Jose, Calif., place bids on behalf of marketers. For example, when a visitor requests a web page, an online ad-exchange quickly runs an auction to a find a bidder. The online ad-exchange provides an online ad requests that bidders bid upon and the winning bidder's online ad is displayed with the web page as an online ad-impression. The visitor could then potentially take a desired action, such as clicking on the online ad, making a purchase on the marketer web site, etc.
Every day there are billions of online ad requests requesting bids for available online ad-slots. Marketers and the automated systems that assist marketers continue to struggle to distinguish and select appropriate online ad requests. For example, showing an online ad about promotional offers in California to a person residing in India would be meaningless. On the other hand, it would make sense to display an online ad about tires on automobile related web pages. Existing provide no way to identify which dimensions (i.e., attributes of the web page and/or visitor) of the online ad requests to use to select online ads bid requests to bid on. Moreover, once a bid request is identified to bid on, an appropriate bid amount must be selected and existing systems.
Existing techniques for selecting online ad requests to respond to and appropriate bidding amounts have deficiencies. For example, contextual techniques serve online ads based on best contextual matching, serving an online ad for a credit card on web pages related to financial articles. However, the contextual associations between the online ads and the web pages are manually identified and based on intuition and assumptions. As a result, online ads are often not optimally placed. In addition, contextual models are generally based on textual analysis of the web pages and hence require extensive crawling of the web pages followed by natural language processing. These processing requirements make contextual techniques impractical in many circumstances. For example, a response to an online ad bid request generally must be made in 200 ms or less, making it infeasible to do contextual analysis of web pages in real-time. Additionally, natural language processing tools come with their own shortfalls making the contextual bids sub-optimal. In certain scenarios based on domain knowledge, human curated lists of websites or website categories have been created where online ads are displayed. However, such an approach only includes certain well-known web-domains while excluding the vast inventory available in the online ad-exchanges. Moreover, the right categories are often not obvious to simple human observation.
Behavior-based ad placement techniques are similarly inefficient. For example, behavior models are created for single or cohorts of visitors based on user attributes (e.g., age, gender, income, etc.) and online ads are served based on visitors having an expected interest that is similar to the online ad's topic. Data for single visitors is, however, often unavailable, changing over time, or so sparse that it is insufficient to statistically deduce useful visitor information.